Harvard Forest >

Summer Research Project 2017

• Title: Group Project: Tree Response to Extreme Events Across Scales and the Use of Data Provenance
• Group Project Leader: Neil Pederson
• Mentors: Emery Boose; Matthew Lau; Neil Pederson
• Collaborators: Emery Boose; Neil Pederson
• Project Description:

  1 Introduction
  Drivers of plant life are diverse (e.g., Billings, 1952) and, given the lack of mobility for plants, can be rather constant. Indeed, the force of an individual driver can vary in strength over time. As important, there can be positive or negative feedbacks among drivers. Understanding the strongest most consistent driver(s); transient, but powerful driver(s); and interactions among multiple drivers will provide significant insight into how forests might change.
  
  In all forests, the number of drivers affecting plant growth is numerous. In temperate, mesic forests, like those in eastern North America, some drivers might be more obvious than others. For example, this region experiences moderate temperatures and, on average, receives what appears to be a plentiful amount of precipitation that is distributed relatively evenly throughout the year. Climate might not appear important, but appearances might not represent reality for a plant.
  
  For this project, the student(s) will statistically analyze the drivers of radial growth of trees through traditional techniques and, in the case of an advanced student, emerging methods and models. A second aspect of the summer position is to help evaluate tools for collecting and visualizing data provenance (automated recording of data sourcing and processing information). Being able to track data from original inputs to final products is essential in allowing for transparency of research and replication in science. Some fundamental questions to investigate are:
  
  1. How do extreme events impact tree growth?
  2. What are the legacies of these events on growth?
  3. How do different tree ring detrending/standardization software methods compare?
  4. Can data provenance improve the transparency of tree ring and climate data?
  
  Since Q1 does not lend itself to an experimental framework, a statistical approach that can account for indirect effects of multiple inter-correlated factors and response variables is essential. Possible statistical avenues to explore:
  
  - Preliminary pairwise statistical analyses using correlation coefficients
  - Structural equation modeling
  - Bayesian hierarchical modeling
  
  2 Materials
  To address these questions, the student will have access to tree-ring data collected from ca 1600 trees distributed over 20 plots from eight forests. Along with these data, ecological attributes were collected and other drivers can be investigated for their interactions to extreme events. Some of these drivers include: forest diversity, forest density, tree size, and tree age. The student will also have access to daily climate data across the study region so that the impact of specific or transient events can be examined (sensu Kim and Siccama 1986).
  
  To go along with this, tree-ring measurements of some species have earlywood, latewood, and full ring widths, which allows for even greater sub-growing-season investigations. With these data, the student has the potential to examine these relations from tree to regional scales within or across species. Time and interest will shape these potentials.
  
  3 Mentors
  - Dr. Neil Pederson, forest ecologist and tree-ring scientist
  - Dr. Matt Lau, quantitative ecologist
  - Dr. Emery Boose, information and systems scientist
  
  4 Opportunities and Responsibilities
  The student(s) will also collaborate with other students conducting traditional dendrochronology or data provenance analysis. The Harvard Forest Summer EXtreme Climate Student(s) will have the opportunity to assist the dendrochronology student in the collection of field samples and lab data so he or she has a firm understanding of how and where the tree-ring data were collected. As a group we will visit old forests to learn more about forest dynamics, competition, and long-term forest development. Student(s) will become familiar with statistical analysis, the R computer language, and associated software. Student(s) will also become familiar with key papers in dendrochronology examining the climatic sensitivity of trees as well as key readings in statistics and data provenance. The work will contribute to a growing body of work examining the dynamics of forests in the northeastern US.
  
  Student responsibilities will include:
  1) learning data management
  2) tree-ring analysis; The student(s) will assist in all aspects of dendroecological investigations from collection, sample preparation, analysis, and synthesis of results
  3) learning and conducting statistical analysis with software program "R"
  4) results will be presented orally and in written form at the annual summer student symposium.
  
  

• Readings:

  5 References and Recommended readings
  C.D. Allen, A.K. Macalady, H. Chenchouni, D. Bachelet, N. McDowell, M. Vennetier, T. Kitzberger, A. Rigling, D.D. Breshears, E.T. Hogg and P. Gonzalez. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest ecology and management, 259(4), 660-684.
  
  W.D. Billings. 1952. The environmental complex in relation to plant growth and distribution. The Quarterly Review of Biology, 27(3), 251-265.
  
  Daniel A Bishop and Neil Pederson. 2015.Regional Variation of Transient Precipitation and Rainless-day Frequency Across a Subcontinental Hydroclimate Gradient. J Extreme Events, 2(2), doi: 10.1142/S2345737615500074.
  
  Emery R. Boose, Aaron M. Ellison, Leon J. Osterweil, Lori a. Clarke, Rodion Podorozhny, Julian L. Hadley, Alexander Wise, and David R. Foster. 2007. Ensuring reliable datasets for environmental models and forecasts. Ecological Informatics, 2:237, doi: 10.1016/j.ecoinf.2007.07.006.
  
  M. Carrer, M. Brunetti, D. and Castagneri. 2016.The Imprint of Extreme Climate Events in Century-Long Time Series of Wood Anatomical Traits in High-Elevation Conifers. Frontiers in plant science, 7.
  
  G.J. Ettl and D.L. and Peterson. 1995. Extreme climate and variation in tree growth: individualistic response in subalpine fir (Abies lasiocarpa). Global Change Biology, 1(3), 231-241.
  
  J.D. Galván, J.J. Camarero and E. Gutiérrez. 2014. Seeing the trees for the forest: drivers of individual growth responses to climate in Pinus uncinata mountain forests. Journal of Ecology, 102(5), 1244-1257.
  
  E. Kim and T.G. Siccama. 1987. The influence of temperature and soil moisture on the radial growth of northern hardwood tree species at Hubbard Brook Experimental Forest, New Hampshire, USA. Proceedings of the International Symposium on Ecological Aspects of Tree-Ring Analysis / compiled by G.C. Jacoby, J.W. Hornbeck.
  
  M. Lindner, M. Maroschek, S. Netherer, A. Kremer, A. Barbati, J. Garcia-Gonzalo, R. Seidl, S. Delzon, P. Corona, M. Kolström and M.J. Lexer. 2010. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management, 259(4), 698-709.
  
  N. Pederson, A.W. D'Amato, J.M. Dyer, D.R. Foster, D. Goldblum, J.L. Hart, A.E. Hessl, L.R. Iverson, S.T. Jackson, D. Martin-Benito, B.C. McCarthy, R.W. McEwan, D.J. Mladenoff, A.J. Parker, B. Shuman, and J.W. Williams. 2015 Climate remains an important driver of post-European vegetation change in the eastern United States. Global Change Biology, 21 (6):2105.
  
  V. Rozas and J.M. Olano. 2013. Environmental heterogeneity and neighbourhood interference modulate the individual response of Juniperus thurifera tree-ring growth to climate. Dendrochronologia, 31(2), 105-113.
  
  N.E. Zimmermann, N.G. Yoccoz, T.C. Edwards, E.S. Meier, W. Thuiller, A. Guisan, D.R. Schmatz and P.B. Pearman. 2009. Climatic extremes improve predictions of spatial patterns of tree species. Proceedings of the National Academy of Sciences, 106, 19723-19728.
  

• Research Category: Regional Studies, Physiological Ecology, Population Dynamics, and Species Interactions, Large Experiments and Permanent Plot Studies, Group Projects, Forest-Atmosphere Exchange, Ecological Informatics and Modelling